{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树的学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "boston = datasets.load_boston()\n",
    "X = boston.data\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学习曲线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于RMSE绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def plot_learning_curve(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "    \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(mean_squared_error(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(mean_squared_error(y_test, y_test_predict))\n",
    "        \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               np.sqrt(train_score), label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               np.sqrt(test_score), label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    \n",
    "plot_learning_curve(DecisionTreeRegressor(), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基于R^2值绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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kaHRZ0dlCVpbbHJmRdmDXOmCHx2sKNblDha+HEXqYilj5K90tF1/Ae5h9T1FWS28tlwlnABD2bBO3CF0d65EObc9UN1DEHOs2BN3Fchk0Bjj3AWDwWHre3aq3M/8PsUj6UafqnMpwHrGglxofPaOX27bk9t72rVTjGQAaD9LrMw2IiHZRZDJ0It1Cb1+lc9fVSbzkd8CDx9NdQ9JD79CTG3CEnn9UTZFEPPU1JTxuw+/d/t8y4VFZMAvLxR+iP7cIvcLvSBHMY4Te24vDjNuphroS0kScMl6caYuqQz/aqb9jdT39Zl27taCbYquWlZ2prCevCN0W3e9NbWuW1iULeqnRbgwM2rSUclyzpc0Q9GMu0+sz5Ysrz7x6CIn6tlWpdwdbP6C/zp06moiHtfCzh55/1NB6s1Nv51r6Pyghdxt+7/b/joUzR+i+YGo5h1g3CbZrhG4VteorQe+t5aL2JeMk5up3VFkuvgBl8JjzhpoeOkDHerCGvr9ZQlcJcKiOHt0E3SsrxhmFx8JZl+llQS81zFGd//guzZeYLW1btKA3TQVOuJqsl3QReiKuBb1qMFW12/OZ9+jS1ma7n64sGY7Q80/CJQp/4UZg3nfTR+jZCLrqnzEFvfEgYNcn9vdFPSJ0Ka0IvS8tlzzuS0387NyvWfTLTdABEv1grbvl4ibobp2itg5V5wTYe923c4EFvdTo2EmT1zrp2p2+B7x5CYmrEnSAZmU5/IupHV1mZ2vHTh2NVw0BaocCHdu1r+hk72a7n65sIfbQe0ciDuz+1LEuZn8E6DgIt+l1bpNMuAl6PGy/sCcjfOP9ww4DWjemDoX3V6ZG6MoG6osIXXXi5zPaj0f0RdDsuDWzdGyCPsTenmBNqoUifHoAU6V1YcjKcnGItmnBsIdeZnS22GcmB8jj+/FY4PWfub9n20rgN6fSweh8b6Ca8mvjMZ16aJ6YHdvtEXrtMBKLlrXun7VxIbB7g9E2Kz+dI/TeseAO4BdH2EU9abkYgh5pt/o8emC5mP93JVxm1D3sUABST+sGaA/dF7Rvqy4ITg+9mLJczH3Fo/o7mxeKSiNCd3roCn+IInGnKIfqaAAToMd6mJ2iiah9flLzvSbmHW+G84gFvdTo3AnUNNjXfbaQHt+6z/095m2yGaEDQLCaOnfe/Dl1am5cZD+x27c5BH0oLW//0P2z3rqPovqDz7KvZw+9d6x/hR7bjQFcScvFFPQOazLnmH0bE1UC1xx/EHNE6HG3CP1QejQv5l5ZLkk/2ppVKGRFukVruRgRurlfW4Terrcxz0G/slycgm78vr4AEKixR+iAFvJItpaLS4epAQt6qdHZQilTJp+9TY/djg6rl24DXr0L2GNEdc4puwJW1TlVo6X5XfsB174jNUIHvAUdAI6/EjjaMVNMvgt17VzTs8FVpYqqXyINO8xNtCMdFKEnLZM0Hrpp3cXC9qH/at9m1D3UTdC7SNAClQ4P3np/RQCY+SPgfGu8Ql6zXPJtubhE6DYPvdPIQ3d66DWpUXao1v5ZlQPpHDXPLyXk6SL0fFsuQoiZQojVQoi1QojrPLb5khBilRBipRDij9nsl+kBHS10MB1wul736Zt6ubWZouyFD5IFs+CHwMqnKII67yFg3Mn2/QWtDivVebN7g/3Ebt9GHaC+EN06KxFIlwO//8n28qSANbuLx4QHubJtJc1M/8bd+dlfKeAm6G62ihL0uIu/ntzGEhR1twWkeujxKF0wzbTF6iEUTOz5jCaOVrnT/kqKRk2xMTNGBjbpNNl8RtX5tG/iUftdhcJpuSTz0A1BH3Yoibeb5WJSOZCibbPPSl1clYiLCv07xiLAB0/ay2pkuNPNaEIJIXwA7gdwOoBmAIuEEPOklKuMbSYAuB7AcVLK3UKIoe57Y3pFtItul2vqgdP/RCfWfVP04BAA2LIcmHshLfuCdMI1LwKGH05D/p0o4VVZKy1r7Sf2zjXkA1YPocfaLP61TUel1m5pfpcqzF22SPuKPUUd8G8/QHnE+wJpI3T1mLAEQmpxcY3Q2+nOzBScWLcjwo7a3ysqyLOuHAhsfg9Y8ntg4rn0WtJyMMQmGe0qvztPg4EAXQwsL/syLBdVdTGd5RIL029hCv2Yo6lOizPLxew4VfvytFysx5pG6niOR6lS43PX6+8rfBk99Gx6FaYBWCulXA8AQoi5AGYDMKs0fQvA/VLK3QAgpeTSeibrXqZ/xv4n9W4/KrOkup4OxMFjab8yDtQfQGJsDjYyT0g1bN+JslzUxBfrXgaG7E/LtcNo/tHR07RVE6rTFwozRxcAzrqHIjHnbOyqjS1rqAO3ziVLJxeUndC5k0SsYh9wDtNF6Or3iHYCsGwoFcl5pS0Gq0mEFeZIRbVv83/rC9GFOFRLd4EAsOrv9BioouPCjB6TnaIOIS9my0UJekU6y8U67s1yGV5ZLoPG2D8rVAusfRH45FVjO2W57KX9+kLA6vnAP64EPnuLzvH27UCigs7HPKQtjgJg1nlsttaZHAjgQCHEm0KIhUKImW47EkLMEUIsFkIs3rFjRxYfXSY8eh7wh3Oy27Z1E/CXr9trJyuSgm556BU+YID1r2g6ik76veZAIwnsZw3Xt603UJaL2dm26Df0OO1bdMCte0kLtBD6xD/8i/RYNUS3Yb9jrTYOAcafZi0bt6fbs6inngmz823nx73fXymg7mrcInQl7G41Wtwi9GgnCZDp8ca6UwU9ZvyfT/ofWg7Wpaas+kO0r7BRKVBZPkl7RA3WKTLLxe+Stuhluaih/74g/T9O/n/A162R26FaGhX9l29QFN/ZklrF1G3sRridbM6lj9Jd9Om30Gcue4yyiabfClzyAnDur2h/eRB0t/tjZ2+UH8AEACcDuBDAb4QQKdNwSykfklJOlVJObWxsdL7MAFS9cOVTwEMnpXYkqsjI7Mw6515g7Al0+1s7jKaUM5l4LjDic8DpHpNCmxH60EOBk67Vr+1/qr7dMyPuA6bTHcFkq+DQhNOBMccCQ8bZ933B48CZP6UDVeEsv5srzs67fWWaO1fLRUXoVmTplsvs1SkaqLFH6PFIatqhitDHHg+c8H1adnb0AZaHXmdPwzPTFoFUYe8NfdYpmiHLJdpJf+oicPK19NsA2q5a+TcS9c4WKoJnMuMOfe6q/+dfvg48eCL9X7/wa+CwLwBXLAOGHQ6ceiNw6LnA8MOAz12QehfkQja/bjOA0cbzJgCbXbZZKKWMAvhECLEaJPCLstg/Y7LxHb28dwvQcIB+/ulbdLCZAjn+FPoDgFdH2CewAMje+PZr3p+nIvRElA4YU7iD1UD9eKrdYq6/6C90AKr5Rfc7Dvj3r6XuO1BFUf57j+p12UyQkY4fDrVHZvvKNHeulosjy8X8LVSqm6fl4uKhq+gzHqF9qzshn2EvODv6AMtysNZH2injxRnt5qv+im1f+bZcZOp+zVGukFTsbGBT6n7GnUiToh8wHVj9DP2/DnQYFfsdQwHOE/9B/8dhh9P5EG6l9crqrBoMfOeN1M845GyyL/G059fJJkJfBGCCEGKcECII4AIA8xzb/B3AKQAghGgAWTDrwdjJlGYnJeWUq6t4h8OW+vQtYNRUXXjfiXPQEGC/ZXTDzEZxCnqgigQdsK+vqKDshfrxdNAqm8WLf/s5cNj5dNBveN29Zkg2qEjUFKl9Jb9dCbqZ0ZLWcskQobt66GF9PCQMy8VviHAwTYQOaKvHTFsE+sZyyee+Hj2P5gsw1wG6rotiyzKgfkLqfg6aBdy8m9IzBzRR5O4cLwLYi+J95w3gf9YBX3oUmHpJ5rYedwVw7GVpN8ko6FLKGIDLADwH4EMAT0gpVwohbhNCKGP4OQAtQohVABYAuEZK6TE2vJ8ItwO/PpWyPoqF1o3pU/d2rScLQQ3KMQU93EbZLKqErRtq0FDIuE2syiDoNUbWilPQ/VXAwNH6NSeBKuCiPwMNLge4SdORdKAf/z3KfnnlzvTbe+E2qGJfGYGaFHTD53YOLLJZLq3210wiHSTMbh66suDMTlFbhJ5J0B0XEiWIQpCoOwWyJ6gOyXzaN4BOgzXXqY5NVf421k12oxtCkEUz5xXgi4+4bzPYYUtWDQYmnpO3jv2s9iKlnC+lPFBKOV5Kebu17iYp5TxrWUopvy+lnCilPFxKOTcvresNG16n+Sxfuq3QLdHcc3j6Wb23WB2h6lbNFPTNy6gXfvTR3u9XaVIHn6nXZYrQA5W609ItQk/OxpJ+hFpWjD8VGHNMqs+fLWaNGMW+UiNGCbp5dxJPJ+hpIvSoZbmkeOgRHaGr54A9o0NZK6YV4a/UQq/+H84sF7VcTPXQAff2mG0esj9wxXLgy4/pdQ0egq6obUxNWVSoux1zHEkeKd98L3N0YzFheuROtiyng0lF4R1Gh5/KNR/xOe/3q+ht9DRdFChThA4AA6yURjdBH2pVc6zJ09CCAaNoGrue4Cbo+0yEbuUm2CwXy4Jys1wyeegBp+ViRehJyyVmROiG6KlIPFSHZL6EM0KPdOqLti1jZGB20x1mIq9+vMtFwblu8H5UmEzhFaFny407gYue6N0+PMjDPUuRouo2ZyNo/Um6IfNblwNDD6aTqnKQPULfsozEsDZNdtCxV1CnyaSv0JD/9q06okpH7TAAH6QKui8AHHQmHXwqBbG31A2njBopcx9g5KzFXeHvuYce6aC7nnQWVjGRleWSrYdupS0OGgNAUDpcLGwX9LiRseJ3sVxCdbRN1y5dnAugWZKemqMvvma0e/HT2Q1My0R1gzVyuTrztplwuyi4rROCrNCP/tl7Qc/HnYUH5RmhJ+I6nS2T5dDf7FpnT7tTSEkR+nArAq9pdAj6+8CISen3XVMPTL+ZTrCqwRQRZePNqagpVJf6ewkBHDgjP94nQHcDse7UujPZYIvQBX3HXCL0RFx3TD/2BeD3Z5ZOp2pvLJdYBNi6wto2oS2XYROBa9YBI6dY1RYdnaLO0Z6AjurNi3+FX0for//M/n8yj5uhB3tbEblwxJeASxe6+/m5Yn43Na7Crb8IAM5/GLh8aWpZiyKiPAX9tiFGKdkiK+AkE+5zQK5/hS5CTUfS85pGslw+eJL+dq4BRhyR/edUD8n+YqYOYF8wf8Ltheq43Zvl9Hldu6luCGAXCn8liYubh965C/j7pakWzX1HAu8+RMuqoFlPM276m1wtl7Bhuax4ksY1dO3WE0ErUaqptyan6KZtkxF6TI8eNgeGmZbLQbOsfVXr9Z0tFMnuZ+VnC1/Pv7MXvoBO8estqmNVVAD//mvge6u87+r9IZ31VaSUr+WicE6LVQgq/PZsg+0fai/89bupNsampTQb0OcuovU1DZTz+tJt1pyGUvvZ2bDfcdl71c4Mhb5EpVa2baEIMRN/+zaw5jmKJG2Cbo1OdMtDX3A7jbQbNQU4ykoHS8SB3Z9YmUTGiD3nPJjFSjaWS7hdl7FNRugxCgwSMbKslGCbUaY/pKN7s1N06wd092YOYU8K+gDg9NuAKRfT62ZK7uFfpLERy5/QJXeLlVAdlcU4/Ta6aA50DoIvLfYBQS+yE9YXtA+uef1ndDLVDAUueEznmNc0Ah//y34CNx6c/eeccn3226qUrP64lUwK+laKLFc9DRz6797WkOpz2Lo8VdCDde6Wy8419GhGlkr4o532juliOz68SGu5GBF61RC6WEaMbJPkfJRW4S5ApycC5EerC4CyVBJRYNsK6gw0+zpMy6XCp/OqzW3GnUh237Rv9fjr9hu+AHBVmn6tEoMFva+RUkdQE2ZQTRUlUpEO+pv8VeD0H9j9xaGH2MXcF8zfbaaTqd+k/U+6qG/2b6Isl7bNwGs/BV69k6LKQ85y375qEND6GfUhmL67itDbXerA7bYm9DB/P1XhLtqlM6CA1FntixXXgUWO8rmRdvpNuqv0941Hje9uVPlzRuhK0FVHYzxKZYqdx0SyUzRNtko+fHKmR7Cg9zXK5zzpOqqT8tS3acQnAGz/CICkvHPnSTDuRPvz+gl9529X+IAjjaH7//1O31lVgSrKUti5lrJwgPSfpbzgLe/bO7CSE/O6DEhWpXvN6D0ZoXfZbZpk7ZESqdpos1zUyFkjy9Pkh28AACAASURBVEXNQK/EOxHTnfARo3hW0MwjD+n/gRL6nWtoe7PMBKCzptw6Dmfeqe/2mIJQAkdwD6jwUzQbrC28h66iKH+IBGPoIcDeZqpo+JtT6TUzx1XRcKBe9lfZhwz3NUMPBkZmyKjpDQecRnaSmhrP9LSlBH73b8DKv5PItlpVIre8n9opGnLpFDVz990mElAFlhTxCO33tsHAG/f0/rv1FW5lD9wsl2CtPZ0vHtEdoZFOPf2cmYPuN0pJqPWb36NH57FZOYDsQbfRwUd/R3eUMgWh/ARdWRyHf5Gq/7nNet6fOIsUqY7NZ4yJGQbtl/o+IchbHn4EpUuZVRBLnYmzyT5RU+N1GLZJ5y7g0zcoC6V9K4lV1RCaHNks2+oPps7jCNi3MYVbLUc67UIfCwMv/YCWVX3vYkQ6MloAd8slWGOv9ROP6kweZfEBdtG3pSVa6zcvJZtn6CH2dvgCwFUfAUe4TJbCFJzys1zMokC+UBFE6I4iReNO1BNDHHs5VWfzutX/4u/6p439zfhTKaNn1zp6bvrgqm57x05dLnjsccCH/7APylJpi5F2u11i2im2ZUv4o0aUCpCgf2jVmsul07m/caYoAqkzFkXarQi9Sm8j44aHbnSK2jx0M0I3Roo2Hmzfl6KiD1IRmbxQfhG6WRTIXwlseBO45wj7zNmFag9AEdB/vkgdoSddR/Nv7msEqoDLFgNXfkC39OYAKjXjUscOKmYG6JzmSLsuPKY6RQG7QJt55UrQP5oPLPuTft2M0Fs/05N7FLq/JR2ulotL+VxnjRZAd3hGOvVvYm5j89OrdAesmxXIFDXlJ+hmUSC/FQnv+VR7ggDw3mNWh2R/tMcRoQM0QGj2/fkZ6VaqVFRQ/nJNI0Xo618BljyiI/TOFj2wZYxRkGzUZHr0hfTAKbMkgCnoKjJ98xc0uEa9bloxG42S/YW259KRtFxc8tDjZqdobRpBNywXU8TNDk5fQNdcz2UgG1MUlKHloiZ6DdhrUPgCwOpn6WR++lLyDW/sh2nwnDO3MHZqh5L18ofZ9PzEa+ixYydFzr4QDU4RFSQ0IyeT+PtDut50505gkFXmN+rSEWrOahS1olR/FXUWNluCXn9AcQt6sva5Sz30RExPEB2sSR1PoATdtFwCHoLuDwGzfkJ3Q0d+PZ/fgOkHyk9llMVR4bMLuvABf7rA2K6fTl7n3IqMnZpGoN24sLY6IvTaYfTbDRhF0btKo/OH6L2APbNFReihAYagOzpKo51Uh6Stiy4mg8fSIKRStlxiXQCku+Wi7MZIB10U/VV2H9zMKfcFgc9/O+/NZ/qH8hN0m+Vi9vYXKPriCD09tUN1Wh0AtFijPBNRyoVW1fkGjyWxUTaLGaGbHryK0KvraTkes1syalh81WBdGqGmUU+9Vqwk0lkuUZ2+GapNtfJULn+kkwTdGcGbF4B8lKRlCkb5qYyZJmgenM7oq78E1m0mcUbjnJ9x2ypQnW1JJRIOmE7rp99KloHqi/CZEbop6NbFoaaRItLuPUgp0NbZAtQO18+DNXQHZ9o1xUbSQ3exXCDtQ/edgp1M2Wy3BN1RdtbmobOglzJl2ClqdEKaEbozfdHvko7VJ+1xmbmF0TQ68pyjHXrQSqxbR+hNR1LKp8qx9lsjRf2VDsvFEq+aBlo2X1N07ASqzYk8aqzRksUcoTsyWgB7CqMqi+BmuSiinTq10cTpoTMlS/kKus9vPzidNTtiXTQicfenPf+sVU8DPz0ovRCoW2W2XNxxmyxguJFdYUbSgL4Q+ytp8JUqM6yIdlEHatVgitBN/1zRvYeKR6kZd4I1luWSo4cej6WfIzafuFouxmd3ZSHoEav/wBnBO7NcmJKl/AQ97khbVDgtl0SMRiRuXpr9vpc+Cnzymn7+3A00mlHVDknXnr6uM16q+F1u8ZuO0svOGW7MCB2gSLxjB9DRQqNMo13W9Go13oIO6KgcsOqfhHL30O+fBtw5Orf39JSEi+XiGqG7WC6Kz96iqeGUVaWwCTpH6KVM+Qm62QlpCrpb3ex0692YdxnwyNn6eaU1yGVPmiifLZfcGfE5YNxJtOwsWpaM0JWgWzM73bU/8JNxFIEGqkjUo53egh6s1uIVrKHINFfLZdc6d99dyvzbN9Ily8WM0E1BTze+oW44MPNH9nVmVO52gWVKhvIT9LhpuRgeulky1STi0RG2eZmOirxQgr57QxbtYUH35Izb7XOZDhgJXPA4cOr/6k5RRdVgmjJthDlVn8NyCVSRSMcjenCSk0CN/p8Ea3U5hp4gJfDiLboO+8IHgB82pr9zc7J7AzD3K94BRk6WS5q69p+7IH1FRO4ULWnKT9DNiNi8fTQr+plEXU6g7R/SlF0v/zD9Z6koMZ2gc9piZo69DLh2g86HrhtBNsCJ16SKkz8IzFmgSyYoy0Wx5zOKztXAmVfvtE/moAjWaEFMWi4uEypnw55PgTd+Dsy1aoevW0CP//xe9vv41/U0AfG6l91fdxtY5Gm5pInQnRdIJ2y5lDTlJ+hmmqB5++h16+0WEanUtzXPp/8sVc41neXCaYvZUzWYSrPmcttf02iPrDctoQjdtNsOOM3a1vDjg9U66g1WW5ZLDhG6KazKXlH7U560itizQR2f5p2KSSbLpX0HAEGf7SboB0yngmhqEnIvuPBWSVN+YWMyqyRgn+ewyyNC97JcAF0oCrBHQ7EwCYaycdJG6CqNsvx+6rxTNdhb0LxwdvDFwxSdjzsROORsYMYdes7LXeuB+6bQdgEzQjcsFyn1dGqPf5Eu6rc4JpsG7BaeSolVdoWKlnPpZPUKOBReA4sqAvTYspbubPxBd8tlyteAiedkboc5lRxTcpSfyiQtDp/94PeM0F3mpFTvsw0ZN0Yz7lhN9WB2WbPlqDKvru1xKc7FuHPs5bkLSnVD6rqANSHIlx/T64SwC12wRh8rwRp9V5CI6bupdHdo5rGhjiH1PnXnlkvEryxBr/e4ZrnE6LuGLUFXtfbdInS3MrhM2VF+gm5aHDZBt06YpmlUllVF325ZCm4nlTkwadnjNGlxuu1T2lN+P3XeOfz83N9T4yHobtQOo9ndO3ZSjXXTQ1fecSycnT1mCrqqlZKM0Hsg6OoO0iuqNwcW7fkM+PCfVLqgZigN7e/eAwy2Jkpxy3IxEwSYsiUrD10IMVMIsVoIsVYIcV2a7c4XQkghxNT8NTFHzE5R0yZRgn7Wz4FjLtXr3SyXuMtoPDNCN/GF0p+4nLbYt5iWixp1Gqh231YI4LgrgDN+oDOUAMtyscQ4W5vEFHS1rPahMk56kjXj9fnKQ4+Fgce+ADx3PT03L15q5itTvEdYUwm6zQFqMv1WnSrKlCwZBV0I4QNwP4BZACYCuFAIMdFluzoA3wXwTr4bmRNmmuDY4/X6iKp1UW0/4F0tF+NEVPW5zQhdrQMo6ktE7X69bV/cKdqnmBH6aGtAUq7ZKoFqbbm4Carb/9Ym6FbapM/qt1ERuiprmwm3DlYnKkLv3gPs/NioaWN0IKt0RGVbjZwMfPM54MK5Os3Ti+OvBL42L3NbmaImmwh9GoC1Usr1UsoIgLkAZrts9wMAPwFQ2DnfzE7I0dOAG1uAoYfq151TdGWyXPZaFfnMCH3PRr2sRjJ63irz0P8+xR/Ssxg1TaNH9T/LFqfl4sRtGkNT0FUevC9Ix0kiqu8AsonSzTlVvbY3LwzBOuCsu2m5xcikGWzMTXvF+8DX/kEjaw+axZ2d+wjZCPooAIaCodlal0QIMRnAaCnlP9PtSAgxRwixWAixeMeOPppcIuGIiJ01XQLOCN0lbdEUZ1WW1DypzeyXqkGp73FrDwt631HTAEAAo46k5+YdVDY4LZdEHGhZp193s9vMcQ1q2RfQ0XntMHrMxkff+K5ezmS5AMDMO9zzyYcZgcvgsZltFqbsyEbQ3S7tyXtQIUQFgJ8DuMplO/ubpHxISjlVSjm1sbEx0+Y9I+4ioKaAB6rtEXomQVdWjXlSmyfzAOva5nWrzJZL31PTSBFx/Xh6nuuM9GaWSyxMoz5VeiPgHqGr4wLQ0XoiplMWVc57JkFPJIAFd+i+gHSWy6SvAl/5KzDlYhpNe+ZPgf94itIVv/y4vV+A2SfJJmxsBmBWIGoCYN7T1gE4DMArgm7rhgOYJ4Q4R0q5OF8NzRq3vG8VoQeqaS5LU+AzWS5uEbqKur/1MrBlObD0kczZCdwp2nfUDacsEX8IuGFb7iVgA1XacolHgI+esb/uFqHbLvCW5RILGxG6JdCZLJf2bcDO1cCpNwIv/yCN5RKn7znBiMynfYser+qn+XGZoicbQV8EYIIQYhyATQAuAHCRelFK2Qog2TMlhHgFwNUFEXPAPSJWAq6yH3KK0K3X3U7qoYfqyaa9TkRzSjymb5h+sxbSQA7peVVD6EIghL1TNNxm384tyjaPB+WhxyMulkuarJndn5KgAyTWXttLSZYLH0NMBjIKupQyJoS4DMBzAHwAHpZSrhRC3AZgsZSyuLrG3SJiFbHVjbCeOyL0RIIid4V5AkccEXqwTt9u+0N6316ZFWo0H3dK9R1D9u/Z+77zJrDrE1r2GZZLiqC7XMzNOzZludgidGW5dAG/nAac9D+pefZ//gqSjmZoAC27ZtlYHaLcD8NkIKsjREo5H8B8x7qbPLY9uffN6gUJl+qGSsBVFoCK0NVM8rEu+yjCZJQfNGZMt07q6sEk6GqCBfU5niP8YnwiFisDRtIfYFgu0VQBj7p46NFuPexezdlpRujKE+/aTZbK1g9SBb19uxEoVHtPsqGOaVF+pZeY/FJ+R0g8CkDYb0+V6A4ZR49K0NVJ57Rd4mESYXPmeHXiVVn1uVVkbnqvru2JcYdoKZC0XNzSFt0i9C6d4ZRcF9bHizpOVAe623iHcLthFaWZBi+Z+sqWC5Oe8hP0RDQ1IlaFlAZbgq4mSVC3xU5Bj4UpWgrW6BMxGaEPse9DiXW6tEWO0Isf03Jx4hWhVzoEPR7Wnewq40Qde2GHoCcS9tLNyQjdTdC5wBuTHeUh6DvXUO3pRJwidGdErCY5UCPpahqBY78LHGbdAqdE6BE6uUJ1jiwXoU9UFaH7M0XoLu1hio90gu5MW5TSitAdlSFjETqWzNRYJejOCN1Zhz+QxnJROeiCI3QmPeUh6GueBxY/TCMEE/HUFEE1EEgJekUF1fNQAzGcJ2w8omeVNyP0QJWeLEGdsEkhSJO2yCmLxY+6MKusExMzo2XRb4A7RgKtm1wsl249p2myrotluTg7Wp0Ruyo/4Gq5cKcokx3lIejqZAm3kcXhrGx41CX0ONAxoa86iZ0pibFIquUS66aO0IBjTksvy2XDm8Cvjqf3c6XF4kf1hbiVDVAX/PYdwDNXka0SD6cO5IlHrDlNq/XxkbRcHILujNjVHKdmhJ5IAC/eCmz7gJ5XlMfpyvQd5aE0pqDHo6kR8QlXAcd/PzV1UPngKRG65aGHanWt82i3FaFX2d/r1Sn6z+9RdkM8whF6KaAuzK0bU19Tx8e2Ffb1podeN5Ki+0iH9sMBoNPDcnEKvIrQzfTXjQuBN+6mqekAjtCZjJTHJV+ljYXbvNME3fLA1SCUlAg9nGq5xLqsCN0anJSM0D2q9KnnMpH7yEWm/wnW0EV6zQupr6lOUXWcKUzLZdAY8roj7R4RukPQzX4b4aPjyBe0e/grn6LH6nq9HcOkoUwEXUXoe61OyCwjmWSE7uiIikcty8X00J0RunUx8Cq7atZRZ0EvfnwB4OjvkGV32Bfsr6m0xW6HoJt11weNoceuPfYCcF0eaYvm82CNNaYhZD+OVj9Lj+piwBE6k4EyE/Q2PTIzG1SE7swzNi2XcLvOavBX6gFI6r1e2RGq3ku4jWdSLxWO/x4w+avAKTfY13tF6LbJJZSg77ZbLmaWi1lX3YzYk3d9RtpiIq6rRqpOfc5DZzJQhoKew0AeFUU584xjKsulhm6jY900RLxueGqEnrRcDAF/5GydLRFpy20We6ZwVA4AZt9PVRtNe8MrQjdLSKhaLF273S0XmbAXgjOrNQaM/hgVGHTu0kP+VfEvHinKZKA8jhBbp2gOQ+39GSL0oFVPeucaYPcnwH7HphF060Tc+THwyWt6XzLBEXopYqv3Y0ToQaPGuBmhq+27Lculwo+U2ixmVG566Oquz5wHV016oYp8AWy5MBkpP0FP5DCQR52QzghdDSxSI0nfn0uP+x1r3B4rD92R5eLMXgA4Qi9FzP+ZynLp3ktRvMIm6MZFO1htVXB0VH40jw1Xy8WI0NstQR9qzPbIlguTgTIR9L36MZ7DUPsKH/ntsS7qzFInUSxCJ/Qoa5KDhfdTXZfhR6TmoavPUgNCnLflAEfopYgpxklB32NVRVTbGIJuzu1peuImps1i6xS1tveFtHXXYc3oZc5CxBE6k4HSF3QpUz30XPK+A1UUod87GfjpBFoXD9PJNXC0LrI04Qy6ADhrqjuzE1wjdBb0ksMUaJXWGnZG6JXA91YC16xLneYQSL2Q71yrO0bN48TWKZomQue0RSYDpS3oiTjwyo9051G4zRrRmYOA+iutCN2YVk5F6ELoSEqlsjkjdMBeVMlN0H1suZQcSUutUldE7N6bGqEPbKI5Tc3/cdAxVkF1Zv7tP2lWIimtAUiWH5+8ABh56B3b6Xn9AXq/bLkwGShtQd/4LvDqj/XzcBsV4lIZB9ngr7SnHCbi2kMHgFk/piJMB5xGz5PRlOmfmoLuYrlwhF56qP/ZwNE6Y8ktQnduD9gFGtDziwLA6z8Dnvov2led1eEZNLZXlkv7Dioip9IhARZ0JiOlLehOT7F7jyXoI7LfR6DSPlK0a7cl6NYJOvWbwLUb9AlbOZAiLnOUoBlZuQk6R+ilh/p/D7IEPZGwOkWN+i02D91F0FWUP/wwqu753feoDMXyuTQitekoAMLeKRoPA81LgPf/SIHEAONYZg+dyUBpC7pZg6WmEWhZR3njA3IQdH+lfT8dO6yh/x4iXNMAXPKCfTShGVmxh14eKDEeOJr6ZTpb6GIdGqCF1YzQzeWgo1M0UEXVPYfsT4OWhk6kaPuk/wEOmgWM/jxt5wvRZ714Mz1Xdosq8+xWl51hDEr7km9aJb6Qrhs9YFT2+whUpU74q/LQvWiaan9u1rHmLJfyQP3/B1kVOvd8RndulQOsqedi9gi94UC9rCLuCTOAze/Zo/oKH3DRn2l/Q/YHLvyT8ZlWZ/6uT4CRU4Czf0HPz/4FMGwiMO7E/H5Hpuwojwi9biQw6SK9PhfLxRmhq8kwchFhfyhDhM6WS8mhIvRB1jy0O1fTY2gAMHqatY1xjPhDOiNKdZyfcj3dzZ16o33fg8YAY49P/Uw1wGhvMzDuBG3rhWrJquHjiMlAeQj6xU8DB83U69XEv9mgInSVibB9JT3W1Ge/D3OEn2uWC0foJYcST1VDf/sqeqweAnz5MeAb/yKhNTnqP633GpH76GnZd9KbUb7ZGcowWVLilosl6P4Q3b4qqhuy34c/RPsJ1NDAj+bFtF7NP5oNZg0O1ywXjqxKjmSEbgn6NutCX91Atst+x6S+5+TrKRtq9FE9+8zhR+hldWfAMDlQOhG6lECk074uKeiV9pK5uczs4rcGFin/vXkRParp6rLBF7BbLiMm2X1TjtBLD2WnhAYAoYFa0GvSBAsVFcCYo3v+meZdIQs60wNKR9CX/gG4YwSwa71ep6JidfJdsRyY82pu+w1YA4vUxSHWTSPynNPVpUOlmwEUoY85GrjuM6B2uL19TOmgLsK+IGVNqVz0XO7+esOgHI4/hrEoHUHf8j49PmfUqlYirDqhBu8HjJyU2379VVbN84ReN2BUbvOA+iutKN8qQxCyRgAqq4UFvfQw54wd2KTXVw/p28895z7gwJn2wl8MkyWlI+iqY+njf9FoTkBH6L0ZuKMidEDnA7d+lts+QnXkv0esC4MS9GSUx4JectSPJ9ujwrhbqxqcfSXPnjLlYkprZJgeUDqCrkRcJiiHF7DqtlS6zxeaLWZGwiHn0OPEc3PbR6iOIvPOFnqu5oBUUR53ipYeR34duHI5LasIPVBTsOYwTDZkJehCiJlCiNVCiLVCiOtcXv++EGKVEGK5EOIlIUT+e3RUpyUA7FpHj9EcC3G5Yb6/cgBwzXrgvP/LbR9K0DusmWVqGulR3TlwhF7aqBRC50QoDFNkZBR0IYQPwP0AZgGYCOBCIcREx2bvAZgqpTwCwJMAfpLvhiYjdICG+AM6Qu8NQSPq8ldSpkEgx32G6mjkYOtGeq46zjhCLw9UhM5D75kiJ5sIfRqAtVLK9VLKCIC5AGabG0gpF0gpVU7hQgBNyDcyTpFuaADQspbWxcK9j9CrBuvlnu5LeeYqA0eltnGEXh4kBb0z/XYMU2CyEfRRADYaz5utdV5cAuBZtxeEEHOEEIuFEIt37NiRfSsBitArfNRZtf1DWhfrtnvgPaHSqJrY02hf1cje9Qk91jgjdBb0kqbOGnl88L8Vth0Mk4FscvPcehyl64ZCfBXAVAAnub0upXwIwEMAMHXqVNd9eCITNDz/gOnAa3cB9xwB7PnUPrquJ5hlcHsboe/eQBeY5KS/QfsjU5r4/MCVH+i+EYYpUrIR9GYA5iiHJgCbnRsJIaYDuAHASVLKsPP1XiMTNODnpGspQv/on7S+tx56XiJ0w3IxT3qO0MsHrq3ClADZWC6LAEwQQowTQgQBXABgnrmBEGIygP8DcI6Ucnv+mwnLcqmgPOALHteReW/F0jZhQS8j9L2b7MO3zdGGDMMwfUxGQZdSxgBcBuA5AB8CeEJKuVIIcZsQwkrcxl0AagH8RQixTAgxz2N3PUfG7ZPkJnO9exmhV+UxQgccETqPFGUYpv/Iany7lHI+gPmOdTcZy9Pz3K5UVKeowtnx2FPMkX89jtCNeSbNWh88UpRhmH6kdEaKpkTolnDms+ZFPiJ0s+6HP0gdubnUhWEYhukhpaM0iYQ9QleWi2sSTg/pqaCbkf3IyXr54LNpujKGYZh+oHQEXcb1rEKA7nyMdOTvM/LhdY+aopfHfJ7+GIZh+oHSsVycHrqK0CPt+fuMfHjd2U43xjAMk2eKR9C3fwQ8cbGe+ceJl4eezwi9N1535UD7nJAMwzD9TPEI+lPfBlY9DWxb4f66M0JX+eP5EPSxJ/R+H1evBf7rzd7vh2EYpocUj4eenB/UI2tFjRRVqKJaZnGtnvKVJ90nd84FrqjIMEyBKR5Bj1q1pr06JtVIUcXAUcDsB6i2S28JVOZeMpdhGKbIKLygd+6iQluRNnruNfuQ00MHgMlf6du2MQzDlBCF99A3LdViDtgnazZxeugMwzCMjcILunOkp/SoqusWoTMMwzBJCm+5OL1rL0HnCJ1hGADRaBTNzc3o7i7vKQErKyvR1NSEQCD70eaFF3Tn0H0vy8WZ5cIwzD5Jc3Mz6urqMHbsWAivPrcSR0qJlpYWNDc3Y9y4cVm/r/CWS4qAp4vQC99chmEKS3d3N+rr68tWzAFACIH6+vqc70IKr5CJuP25Z4TOHjrDMEQ5i7miJ9+x8IIunYLOHjrDMExPKLygc4TOMEwJsWfPHjzwwAM5v+/MM8/Enj17+qBFmsILekqE7pWHnuAInWGYguMl6PF43GVrzfz58zFo0KC02/SWwme5JGKOFeny0At//WEYpni49R8rsWpzL+swOZg4cgBuPvtQz9evu+46rFu3DpMmTUIgEEBtbS1GjBiBZcuWYdWqVTj33HOxceNGdHd344orrsCcOXMAAGPHjsXixYvR3t6OWbNm4fjjj8dbb72FUaNG4emnn0ZVVe9nXyu8QiYcETmPFGUYpoi58847MX78eCxbtgx33XUX3n33Xdx+++1YtWoVAODhhx/GkiVLsHjxYtx7771oaWlJ2ceaNWtw6aWXYuXKlRg0aBD++te/5qVthY/Qs+0UZQ+dYRgH6SLp/mLatGm2XPF7770XTz31FABg48aNWLNmDerr623vGTduHCZNmgQAOPLII7Fhw4a8tKXwgp7SKcpZLgzDlA41NTXJ5VdeeQUvvvgi3n77bVRXV+Pkk092zSUPhXRVWZ/Ph66urry0pfCWS7adohyhMwxTBNTV1aGtrc31tdbWVgwePBjV1dX46KOPsHDhwn5tW/FF6J4jRTnLhWGYwlNfX4/jjjsOhx12GKqqqjBs2LDkazNnzsSDDz6II444AgcddBCOPvrofm1b4QXdGZGnjdALf0PBMAzzxz/+0XV9KBTCs88+6/qa8skbGhqwYoWeavPqq6/OW7sKr5DOtEX20BmGYXpEEQg6e+gMwzD5oPCCrjpFp99qPec8dIZhmJ6QlaALIWYKIVYLIdYKIa5zeT0khPiz9fo7QoixWbdARegDm6wVXnnoXA+dYRgmHRkFXQjhA3A/gFkAJgK4UAgx0bHZJQB2SykPAPBzAD/OugUqQlfRN0foDMMwPSKbCH0agLVSyvVSygiAuQBmO7aZDeARa/lJAKeJbIv5qqH/FdY0S2lHihbeIWIYhilWslHIUQA2Gs+brXWu20gpYwBaAdQ7toEQYo4QYrEQYvGOHTtopcpy8SlB5widYZjipaflcwHgnnvuQWdnZ55bpMlG0N0ibWcYnc02kFI+JKWcKqWc2tjYaK1Ulovf6216O/bQGYYpMMUs6NkMLGoGMNp43gRgs8c2zUIIP4CBAHZl1YKEQ9DdLBcpKXLnCJ1hGJNnrwO2fpDffQ4/HJh1p+fLZvnc008/HUOHDsUTTzyBcDiM8847D7feeis6OjrwpS99Cc3NzYjH47jxxhuxbds2bN68GaeccgoaGhqwYMGC/LYb2Qn6IgAThBDjAGwCcAGAixzbzAPwNQBvAzgfwMtSepnhDlSEns5yUes4QmcYpsDceeedWLFiBZYtW4bnn38eGVBOtgAAB4BJREFUTz75JN59911IKXHOOefgtddew44dOzBy5Eg888wzAKjGy8CBA3H33XdjwYIFaGho6JO2ZRR0KWVMCHEZgOcA+AA8LKVcKYS4DcBiKeU8AL8F8KgQYi0oMr8g6xZk0ymajOK5U5RhGIM0kXR/8Pzzz+P555/H5MmTAQDt7e1Ys2YNTjjhBFx99dW49tprcdZZZ+GEE07ol/ZkVctFSjkfwHzHupuM5W4AX+xRC7JJW1TbcITOMEwRIaXE9ddfj29/+9spry1ZsgTz58/H9ddfjzPOOAM33XSTyx7yS+FD3kQMgDD88XQROgs6wzCFxSyfO2PGDDz88MNob28HAGzatAnbt2/H5s2bUV1dja9+9au4+uqrsXTp0pT39gWFr7ao0hFVjjlH6AzDFDFm+dxZs2bhoosuwjHHHAMAqK2txWOPPYa1a9fimmuuQUVFBQKBAH71q18BAObMmYNZs2ZhxIgRBesU7VuS6YhW5qOboHOEzjBMEeEsn3vFFVfYno8fPx4zZsxIed/ll1+Oyy+/vM/aVTjLpW0r8Pb9LhG6W9oiZ7kwDMNkonCC3t0KrHtZF91KZ7lwlgvDMExGCqeQogKIhY0IXQ02dYvQ2UNnGEaT7TCXUqYn37GAgi6AeJSyXDJZLuyhMwxjUVlZiZaWlrIWdSklWlpaUFlZmdP7CtcpKgQQD2fXKcoROsMwFk1NTWhubkaywF+ZUllZiaampswbGhQwy6XCitAdlgtH6AzDpCEQCGDcuHGFbkZRUljLJRY2OkVdIvRbBgKv3MlZLgzDMFlQYA89YkXoFcbkFY4I/ZUfcZYLwzBMFhRQIS1BVx562pGiHKEzDMNkQhSqp1gI0QZgdUE+vGc0ANhZ6EbkALe3b+H29i3cXm/2k1I2ur1QyKH/q6WUUwv4+TkhhFjM7e07uL19C7e3bymW9rIpzTAMUyawoDMMw5QJhRT0hwr42T2B29u3cHv7Fm5v31IU7S1YpyjDMAyTX9hyYRiGKRNY0BmGYcqEggi6EGKmEGK1EGKtEOK6QrQhE0KIDUKID4QQy4QQi611Q4QQLwgh1liPgwvYvoeFENuFECuMda7tE8S91u+9XAgxpUjae4sQYpP1Gy8TQpxpvHa91d7VQojUqV/6vr2jhRALhBAfCiFWCiGusNYX5W+cpr1F+RsLISqFEO8KId632nurtX6cEOId6/f9sxAiaK0PWc/XWq+PLZL2/l4I8Ynx+06y1hfmeJBS9usfAB+AdQD2BxAE8D6Aif3djizauQFAg2PdTwBcZy1fB+DHBWzfiQCmAFiRqX0AzgTwLKik5dEA3imS9t4C4GqXbSdax0UIwDjrePH1c3tHAJhiLdcB+NhqV1H+xmnaW5S/sfU71VrLAQDvWL/bEwAusNY/COA71vJ/A3jQWr4AwJ/7+ff1au/vAZzvsn1BjodCROjTAKyVUq6XUkYAzAUwuwDt6AmzATxiLT8C4NxCNURK+RqAXY7VXu2bDeAPklgIYJAQYkT/tJTwaK8XswHMlVKGpZSfAFgLOm76DSnlFinlUmu5DcCHAEahSH/jNO31oqC/sfU7tVtPA9afBHAqgCet9c7fV/3uTwI4TYjkrDh9Tpr2elGQ46EQgj4KwEbjeTPSH3iFQgJ4XgixRAgxx1o3TEq5BaATCMDQgrXOHa/2FfNvfpl1S/qwYWEVVXut2/vJoKis6H9jR3uBIv2NhRA+IcQyANsBvAC6S9gjpYy5tCnZXuv1VgD1hWyvlFL9vrdbv+/PhRAhZ3st+uX3LYSgu11VizF38jgp5RQAswBcKoQ4sdAN6gXF+pv/CsB4AJMAbAHwM2t90bRXCFEL4K8ArpRS7k23qcu6fm+zS3uL9jeWUsallJMANIHuDg5J06aia68Q4jAA1wM4GMBRAIYAuNbavCDtLYSgNwMYbTxvArC5AO1Ii5Rys/W4HcBToANum7ptsh63F66Frni1ryh/cynlNuskSQD4NfQtf1G0VwgRAInj41LKv1mri/Y3dmtvsf/GACCl3APgFZDXPEgIoWpMmW1Kttd6fSCyt/DyitHemZbVJaWUYQC/Q4F/30II+iIAE6ze7CCog2NeAdrhiRCiRghRp5YBnAFgBaidX7M2+xqApwvTQk+82jcPwMVWz/vRAFqVbVBIHJ7ieaDfGKD2XmBlNowDMAHAu/3cNgHgtwA+lFLebbxUlL+xV3uL9TcWQjQKIQZZy1UApoN8/wUAzrc2c/6+6nc/H8DL0up9LGB7PzIu7gLk95u/b/8fD/3R8+r8A/UAfwzyzG4oRBsytG9/UAbA+wBWqjaCPLuXAKyxHocUsI1/At1CR0HRwCVe7QPd/t1v/d4fAJhaJO191GrPctAJMMLY/garvasBzCpAe48H3SIvB7DM+juzWH/jNO0tyt8YwBEA3rPatQLATdb6/UEXlrUA/gIgZK2vtJ6vtV7fv0ja+7L1+64A8Bh0JkxBjgce+s8wDFMm8EhRhmGYMoEFnWEYpkxgQWcYhikTWNAZhmHKBBZ0hmGYMoEFnWEYpkxgQWcYhikT/j8JEbQerwC4mQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def plot_learning_curve_r2(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "    \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(r2_score(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(r2_score(y_test, y_test_predict))\n",
    "        \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               train_score, label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)], \n",
    "                               test_score, label=\"test\")\n",
    "    plt.legend()\n",
    "    plt.axis([0, len(X_train)+1, -0.1, 1.1])\n",
    "    plt.show()\n",
    "    \n",
    "plot_learning_curve_r2(DecisionTreeRegressor(), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以max_depth参数为例，看不同参数学习曲线的不同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=1), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=3), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=5), X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_learning_curve_r2(DecisionTreeRegressor(max_depth=20), X_train, X_test, y_train, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.8 64-bit ('anaconda': conda)",
   "language": "python",
   "name": "python36864bitanacondaconda085db2e8b14649f4b32196068f7124e9"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}